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app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from gensim import corpora, models
+from PIL import Image
+
+# Load the saved models and data
+dictionary = joblib.load('doc2bow.sav')
+lda_model = joblib.load('ldamodel.sav')
+
+# Function to preprocess input text and get topic distribution
+def get_topics(text):
+    bow_vector = dictionary(text.split())
+    topics = lda_model[bow_vector]
+    return topics
+
+# Function to get top keywords for a topic
+def get_top_keywords(topic, num_keywords=10):
+    topic = lda_model.show_topic(topic, topn=num_keywords)
+    keywords = [f"{word} ({weight:.3f})" for word, weight in topic]
+    return keywords
+
+# Streamlit app
+def main():
+    st.title("Web Berita Topic Clustering Untuk Program Kedaireka UMKM📰")
+
+     # Sidebar with title and description
+    st.sidebar.title("Topic Clustering")
+    st.sidebar.write("Discover topics in news articles.")
+    
+    # Input text area for user to enter their text
+    user_input = st.text_area("Enter your text here:", "")
+    
+    # Submit button
+    if st.button("Submit"):
+        if user_input:
+            # Process the user's input and get topic distribution
+            topics = get_topics(user_input)
+            
+            # Display the top topics
+            st.subheader("🔥Top Topics🔥")
+            for topic in topics:
+                st.write(f"**📍Topic {topic[0] + 1}** (Score: {topic[1]:.4f})")
+                top_keywords = get_top_keywords(topic[0])
+                st.markdown(", ".join(top_keywords))
+                st.write("---")
+
+    # Add a footer
+    st.sidebar.markdown("---")
+    st.sidebar.write("© 2023 Web Berita Topic Clustering")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+scikit-learn
+streamlit
+pandas
+joblib
+gensim
+plotly